plot-methods              package:rrcov              R Documentation

_M_e_t_h_o_d_s _f_o_r _F_u_n_c_t_i_o_n '_p_l_o_t' _i_n _P_a_c_k_a_g_e '_r_r_4_c_o_v'

_D_e_s_c_r_i_p_t_i_o_n:

     Shows the Mahalanobis distances based on robust and/or classical
     estimates  of the location and the covariance matrix in different
     plots.  The following plots are available:

     - index plot of the robust and mahalanobis distances

     - distance-distance plot

     - Chisquare QQ-plot of the robust and mahalanobis distances

     - plot of the tolerance ellipses (robust and classic)

     - Scree plot - Eigenvalues comparison plot

_U_s_a_g_e:

     ## S4 method for signature 'Cov':
     plot(x, which = c("all","distance","qqchi2","tolellipse","screeplot"), 
             ask=(which=="all" && dev.interactive()), 
             cutoff, id.n, tol=1e-7, ...)
     ## S4 method for signature 'CovRobust':
     plot(x, which = c("all","dd","distance","qqchi2","tolellipse","screeplot"), 
             classic=FALSE, ask=(which=="all" && dev.interactive()), 
             cutoff, id.n, tol=1e-7, ...)

_A_r_g_u_m_e_n_t_s:

       x: an object of class '"Cov"'  or '"CovRobust"'

   which: Which plot to show? See Details for description of the
          options. Default is 'which'="all". 

 classic: whether to plot the classical distances too. Default is
          'classic'=FALSE. 

     ask: logical; if 'TRUE', the user is _ask_ed before each plot, see
          'par(ask=.)'.  Default is 'ask = which=="all" &&
          dev.interactive()'.  

  cutoff: The cutoff value for the distances.  

    id.n: Number of observations to identify by a label. If not
          supplied, the number of observations with distance larger
          than 'cutoff' is used.  

     tol: tolerance to be used for computing the inverse see 'solve'.
          Default is 'tol = 10e-7'

    ... : other parameters to be passed through to plotting functions. 

_M_e_t_h_o_d_s:


     _x = "_C_o_v", _y = "_m_i_s_s_i_n_g" Plot mahalanobis distances for 'x'.

     _x = "_C_o_v_R_o_b_u_s_t", _y = "_m_i_s_s_i_n_g" Plot robust and classical
          mahalanobis distances for 'x'.

_E_x_a_m_p_l_e_s:

     data(hbk)
     hbk.x <- data.matrix(hbk[, 1:3])
     cv <- Cov(hbk.x)
     plot(cv)
     rcv <- CovMest(hbk.x)
     plot(rcv)

